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Simultaneous Localization of Robots and Mapping of Wireless Sensor Nodes

  • Andrea Zanella
  • Emanuele Menegatti
Part of the Studies in Computational Intelligence book series (SCI, volume 554)

Abstract

This chapter presents the use of a mobile robot to solve the problem of node localization in Wireless Sensor Network (WSN). The algorithms we propose are inspired by the algorithms developed in robotics to solve the robot localization problem exploiting landmarks in the environment. The robotics community developed algorithms of Simultaneous Localization and Mapping (SLAM), in which the robot pose is estimated while simultaneously mapping the position of the landmarks in the environment. Similarly, we simultaneously estimate the robot pose with the position of the nodes of a WSN using range measurements. The assumption is that a mobile robot can estimate the distance to nearby nodes of the WSN by measuring the Radio Signal Strenght (RSS) of the received radio messages. The intrinsic variability of RSS measurements due to interferences and reflections of radio signals, however, makes the ranging measure very noisy, thus limiting the accuracy of simple localization techniques. We first present a SLAM technique based on an Extended Kalman Filter (EKF-SLAM) to integrate RSS measurements from the different nodes over time, while the robot moves in the environment. Successively, we show that combining the EKF-SLAM algorithm with an initialization phase based on a Delayed Particle Filter (DPF) can greatly improve the performance of the algorithm. We then discuss possible extensions of the approach by using advanced RSS measurement techniques, and multidimensional scaling localization. Finally, we compare the different approaches on the same experimental testbed, both for indoor and outdoor scenarios.

Keywords

Sensor Node Wireless Sensor Network Mobile Robot Mobile Node Extend Kalman Filter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Department of Information EngineeringUniversity of PadovaPadovaItaly

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